X_v

X= 

<132x100 sparse matrix of type '<class 'numpy.float64'>'
	with 2637 stored elements in Compressed Sparse Row format>

　　

vectorizer = 

vectorizer =  TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',
        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',
        lowercase=True, max_df=0.5, max_features=100, min_df=2,
        ngram_range=(1, 1), norm='l2', preprocessor=None, smooth_idf=True,
        stop_words=None, strip_accents=None, sublinear_tf=False,
        token_pattern='(?u)\\b\\w\\w+\\b', tokenizer=None, use_idf=True,
        vocabulary=None)